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FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to

Anatoly Sorokin1, Igor Goryanin1,2,3

  • 1Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan.

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This study introduces FBA-PRCC, a novel method using global sensitivity analysis to identify crucial reactions in whole-genome models. It efficiently pinpoints key metabolic steps for improved cell growth and metabolite production.

Keywords:
FBAconstraint-based modelglobal sensitivity analysismetabolic networkwhole-genome model

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Whole-genome models (GEMs) are essential tools in systems biology, biotechnology, and medicine.
  • Automated GEM construction often introduces redundant reactions, complicating analysis.
  • Model nonlinearity hinders the assessment of reaction significance for cellular functions.

Purpose of the Study:

  • To develop a novel, parallelizable global sensitivity analysis (GSA) method for GEMs.
  • To efficiently identify critical reactions influencing cell growth and metabolite production.
  • To provide an interpretable metric for evaluating reaction contributions.

Main Methods:

  • Application of global sensitivity analysis (GSA) to genome-scale models (GEMs).
  • Utilizing a straightforward and parallelizable computational approach.
  • Employing the Partial Rank Correlation Coefficient (PRCC) metric.

Main Results:

  • Partial Rank Correlation Coefficient (PRCC) effectively identifies key metabolic network steps.
  • Significance of reactions is captured irrespective of their distance to product synthesis.
  • The FBA-PRCC method demonstrates efficiency in analyzing large-scale models.

Conclusions:

  • FBA-PRCC offers a fast, interpretable, and reliable metric for analyzing GEMs.
  • It accurately identifies the contribution of reactions to cellular functions.
  • This method aids in understanding and optimizing metabolic networks.